Scalable and cost-effective solutions are of the foremost importance for the deployment of distributed/cell-free massive MIMO (CF-mMIMO) networks. Moreover, in the quest for cheaper hardware, power amplifiers (PAs) are expected to be operated on highly non-linear operation regimes, where strong distortions are impacting the system's performance. In this paper, we first analyze the impact of the hardware impairment (HWI) caused by the PAs on an orthogonal frequency division multiplexing (OFDM) based CF-mMIMO system. Then, we introduce a novel sequential precoding scheme aiming at cancelling the PA-induced HWI by taking advantage of a serial fronthaul topology. Building on the so-called Bussgang decomposition, we derive a HW-aware precoding, in the form of a closed-form linear system equation. It takes advantage of a serial fronthaul connecting multiple APs, which offers the possibility of communicating HWI information between successive APs in a branch. Numerical results show that the proposed method is able to provide substantial improvement of the achievable spectral efficiency (SE) when the the PA is operated near its saturation region, where its power efficiency is high. Most importantly, it shows promising outcomes to reach high energy efficiency in CF-mMIMO system while maintaining network scalability.

Cell-free massive MIMO (CF-mMIMO) technology has proved to offer significant gains in terms of spectral efficiency (SE). However, it can be economically attractive only when adopting low cost and energy-efficient hardware. This hardware includes components such as low-resolution digital-to-analog converters (DACs), and causes signal distortions called hardware impairments (HWI). In this paper, first, we propose an analytical characterization for low-resolution DAC where the exact behavior can be described, allowing to shine a new light on DAC-induced distortions. Based on the well-known Bussgang decomposition, we derive for a known signal distribution through closed-form expressions based on the integer and fractional resolution separately for downlink SE performance of orthogonal frequency multiplexing (OFDM) based CF-mMIMO. Moreover, we propose a new CF-mMIMO-specific framework to observe the out-of-band (OOB) radiations on potential victim users. We point out that OOB radiations is less of a problem in multi-user CF-mMIMO, where the total OOB radiated power is sufficiently low to respect any standard mask. Meanwhile, inband distortions represent a serious problem that have to be tackled.

In this paper, we jointly design linear partial zero-forcing (PZF) precoding and peak-to-average power ratio (PAPR) reduction techniques in downlink cell-free massive MIMO systems based on orthogonal frequency-division multiplexing (CF-mMIMO-OFDM). The key idea is to perform a local PAPR reduction of transmitted signals while exploiting the features of the emerging local PZF precoding scheme. Specifically, we anticipate the power amplifier (PA)-induced distortions by transmitting low-PAPR, i.e, hardware friendly, signals. Then the distortions related to PAPR reduction is controlled in the way that is precoded to be transmitted onto a space where the communication is weak, while protecting the strong communications. The joint PZF and PAPR reduction is formulated as a simple convex optimization problem solved via an efficient and steepest iterative algorithm. Interestingly, the proposed scheme is implemented in a distributed and scalable manner, demonstrating the benefits of PZF based CF -mMIMO-OFDM. The simulation results show that the proposed method holds significant promise in effectively reducing the substantial effects of non-linear distortion introduced by PAs at the access points. In addition, we provide the values of the optimal grouping thresholds that yield the highest achievable spectral efficiency.

In this paper, we investigate the performance of orthogonal frequency division multiplexing (OFDM)-based cell-free massive MIMO (CF-mMIMO-OFDM) systems which are subjected to non-linear power amplifiers (NL-PAs) at user equipments (UEs). To counteract non-linear distortions (NLD) introduced by those NL-PAs in the uplink (UL) transmission chain, we propose a joint partial full-pilot zero forcing (P-FZF) combining and an iterative NLD cancellation algorithm for the UL data detection in a CF-mMIMO-OFDM systems. In order to improve scalability and reduced latency, the proposed technique is performed in a distributed manner, i.e. locally, at APs. Moreover, for P-FZF combining, we adopt an adaptive grouping strategy that adapts to changing channel conditions and maximizes spectral efficiency (SE), jointly with NLD cancellation. The simulation results show that the proposed technique holds considerable promise in alleviating the substantial impact of NLD, leading to an improvement of the achievable SE of the considered system. Furthermore, we provide the optimal grouping threshold value leading to the highest attainable SE.

In this paper, we study a meta-learning based neural network (NN) model to enhance the energy-efficiency related to power amplification in wireless communication systems. Specifically, we introduce a low-complexity adaptive solution to perform digital pre-distortion (DPD) for power amplifiers (PAs) using neural networks. Thus, we design a dedicated NN architecture to derive pre-distortion functions using few neurons. Moreover, we develop a meta-learning training approach that allows better generalization and faster adaptation, over time-varying PAs, compared to classical DPD schemes. Thereby, we propose a new approach to realize an adaptive digital predisorter based on meta-learning. A dedicated architecture allows to achieve lowcomplexity while meta-learning permits adapting to most parameters change in the system using few data. Through the simulation results, we have shown that this approach can offer a metatrained DPD function that can provide satisfying performance for different PA models using only 3000 IQ symbols during training phase. Contrary to fixed DPD architectures, the performance of our meta-trained DPD can be improved through only few gradient steps and over few samples during online calibration, achieving excellent performance with moderate complexity.

Neural networks (NNs) based digital predistortion (DPD) have been shown to be a very promising technique to enhance power amplifier linearity. However, studies consider high level of quantization NNs (32-bits) whose hardware implementation is not feasible in practice. This paper addresses the challenge of low level quantization of NNs based DPD for PA linearization in OFDM communication systems. The goal is to improve the overall inference time, resources and energy efficiency. To perform quantization, we first operate a post training quantization to assess the impact of quantization on the NN. Second, we perform a quantization aware training to cope with the quantization noise. Thus, we truly believe that the proposed quantization approach puts forward an efficient transmission chain using NN DPD for OFDM based wireless communication systems. Indeed, numerical simulations show that using 4-bits quantization, resource usage is reduced by 62% compared to the 32-bits quantization. Moreover, the error vector magnitude is still lower than −35dB which is a slight degradation compared to 32-bits quantization.

This paper introduces a new efficient autopre-coder (AP) based deep learning approach for massive multiple-input multiple-output (mMIMO) downlink systems in which the base station is equipped with a large number of antennas with energy-efficient power amplifiers (PAs) and serves multiple user terminals. We present AP-mMIMO, a new method that jointly eliminates the multi-user interference and compensates the severe nonlinear (NL) PA distortions. Unlike previous works, AP-mMIMO has a low computational complexity, making it suitable for a global energy-efficient system. Specifically, we aim to design the PA-aware precoder and the receive decoder by leveraging the concept of autoprecoder, whereas the end-to-end massive multi-user (MU)-MIMO downlink is designed using a deep neural network (NN). Most importantly, the proposed AP-mMIMO is suited for the varying block fading channel scenario. To deal with such scenarios, we consider a two-stage precoding scheme: 1) a NN-precoder is used to address the PA non-linearities and 2) a linear precoder is used to suppress the multi-user interference. The NN-precoder and the receive decoder are trained off-line and when the channel varies, only the linear precoder changes on-line. This latter is designed by using the widely used zero-forcing precoding scheme or its low-complexity version based on matrix polynomials. Numerical simulations show that the proposed AP-mMIMO approach achieves competitive performance with a significantly lower complexity compared to existing literature.

This paper presents a localization algorithm based on the identification of a linear expression connecting a vector of mean received powers to a vector of Cartesian coordinates. The linear expression is based on a Taylor expansion of the received power collected on a set of measurement points. The terms of the Taylor expansion are then integrated in a transfer matrix able to predict a vector of received power from a composite vector of Cartesian coordinates. Using the pseudo inverse of this matrix it is then possible to find the Cartesian coordinates of any unknown reception point, from the vector of mean received powers at this point.

The necessity of supporting the expected 5G's services, which reveal major limits to ensure strict synchronism and orthogonality, has brought researchers to conceive new waveforms. Recently, the Ping-pong Optimized Pulse Shaping (POPS) algorithm [1] has been introduced as a powerful tool to efficiently design waveforms candidates for 5G. In this paper, we characterize a new design of PHYDYAS waveforms, namely POPS-PHYDYAS, which it is based on the mix of the POPS algorithm and the PHYDYAS waveform [2]. These new waveforms ensure higher SINR level compared to the original PHYDYAS waveforms, as they are conceived by Mirabbasi-Martin [3]. Performances of POPS-PHYDYAS are compared to several 5G waveforms in terms of SIR, OOB emissions and BER.

In this paper, the error performance of linear (MMSE-LE) and widely linear (MMSE-WLE) equalizers is analyzed in terms of mean square error (MSE) and bit error rate (BER). This analysis is done in a system using improper modulations of type Pulse Amplitude Modulations (PAM) having inter-symbol-interferences (ISI) and/or inter-user-interferences (IUI). For this scenario, we derive theoretical expressions of both MSE and BER for the two equalizers. Analytical expressions developed in this paper are verified by computer simulations. It is worth mentioning that these expressions are valid for any number of interferers and any channel delay spread.

When non second order circular (NSOC) signals are transmitted over time/frequency varying channels, classical linear equalization (LE) suffers from performance loss due to the signal's impropriety. For such signals, and signals corrupted by one single improper interference, it has been demonstrated that widely linear (WL) processing can enhance equalization performance. In this paper, we will study the limit of WL processing as a function of the number of interferers. We will carry simulations corroborated by analytical analysis and show that the performance of WL processing, in terms of bit error rate, decreases as the number of improper interferences increases.

In this paper, a new widely linear equalizer for single carrier systems using frequency domain equalization (SC-FDE) that takes into account up to K known interferers in the symbol estimation process is proposed. It was found out that the error performance of the proposed widely linear equalizer is nearly immune to the effect of one interferer, regardless of its power; when compared to its strictly linear version, the proposed structure also has a better error performance for up to three interferers.

This paper presents a comparison study of different similarity metrics used for RSSI fingerprint based indoor localization. These metrics are used for nearest neighbor search which is a crucial step in fingerprint localization system. Including Euclidean distance, Manhattan distance and Gauss distance, the present study compares the localization error respect to a proposed parameter named “error density”. This latter is related to the location error and the size of the studied area. In addition, different methods of combining the locations of neighbors have been introduced to estimate the current position and their performances have been compared. Extensive implementation details are discussed and simulation is conducted to compare them. Obtained results show that the Kernel method combined with the weighted average exhibits the localization accuracy on the studied dataset.

This study analyses the bit‐error‐rate (BER) performance of filter bank multicarrier (FBMC) systems with offset quadrature amplitude modulation in the presence of phase offset. The authors consider that the FBMC intrinsic‐interference has a Gaussian probability distribution, and they derive the BER analytical expressions for multilevel modulation over Rayleigh flat fading channels. Analytical expressions developed in this study are checked and confirmed by computer simulations.

Filter Bank based Multicarrier communication with Offset Quadrature Amplitude Modulation (FBMC/OQAM) has been gaining considerable interest as an important competitor to Orthogonal Frequency Division Multiplexing system (OFDM). However, FBMC/OQAM signal inherits the same drawback of High Peak to Average Power Ratio as OFDM signal. Because of the overlapping structure of FBMC/OQAM symbols, the direct application of the conventional PAPR reduction scheme originally proposed for OFDM is not effective. In this paper, we investigate the performance of Smart Gradient Project Active Constellation Extension (ACE-SGP) and Tone Reservation (TR) approaches in PAPR reduction for FBMC/OQAM signals. Indeed, we introduce three PAPR reduction schemes for FBMC/OQAM system based on ACE-SGP, TR and a hybrid one. The performance of the three schemes are presented and compared with those of the OFDM system.

Filter Bank Multicarrier with Offset Quadrature Amplitude Modulation (FBMC/OQAM) is recognized as an appropriate modulation scheme for 5G wireless technologies. With an extremely low Adjacent Channel Leakage Ratio (ACLR), FBMC/OQAM scheme is a suitable candidate for cognitive radio (CR) applications. Like all multicarrier systems, FBMC/OQAM suffers from the high Peak to Average Power Ratio (PAPR). Because of its overlapping signal structure, the direct application of the PAPR reduction schemes proposed for the Orthogonal Frequency Division Multiplexing (OFDM) signal to FBMC/OQAM one is not effective. In this investigation, the main contribution is a novel PAPR reduction scheme based on the extension of the current Tone Reservation (TR) scheme as used in OFDM to FBMC/OQAM. Based on simulation results, the proposed scheme shows almost the same PAPR reduction performance when compared with that of the conventional TR method originally proposed for OFDM.

In this paper, we investigate receiver techniques for nonlinear high power amplifier distortion compensation in Orthogonal Frequency Division Multiplexing (OFDM) systems. We introduce a new method for the detection of the nonlinearly distorted symbols received by the OFDM transmission system receiver. The proposed approach allows the determination of the position of the symbol in error and based on the Bussgang theorem application and the fast fourier transform (FFT), we create a set of a simultaneous equations which, when solved, return the true symbols before they were distorted. The numerical performance evaluation shows that the proposed method enables significant performance improvement compared to conventional receivers.

The Filter Bank Multi-Carrier (FBMC) along with Offset Quadrature Amplitude Modulation (OQAM), is emerging to be one of the serious candidates for the next generation radio access technology. This multi-carrier modulation technique, and like its counterparts, suffers from high Peak to Average Power Ratio (PAPR). When a FBMC/OQAM modulated signal crosses a nonlinear Power Amplifier (PA), the transmitted signal exhibits in-band as well as out-of-band distortions. In such a scenario, it's mandatory to add dedicated signal processing, at the transmitter or at the receiver side, in order to reduce the impact of the PA nonlinear distortion. According to the Bussgang's theorem, the in-band distortion induced by a nonlinear PA can be modeled with a complex gain and an independent additive noise term. The complex gain can be computed from analytical formula or estimated jointly with the channel response. However, for the additive noise we will investigate, in this paper, the use of an iterative technique for PA Nonlinearity Cancellation (PANC), at the receiver side. The Bit Error Rate (BER) performance of the FBMC/OQAM system in presence nonlinear PA and iterative PANC receiver will be evaluated over Rayleigh fading channel.

The Filter Bank Multicarrier with Offset Quadrature Amplitude Modulation (FBMC-OQAM) is emerging as one of the alternatives to Orthogonal Frequency Division Multiplexing (OFDM) for next generation broadband wireless access systems. In this paper, we focus on the nonlinear distortion effects in a FBMC-OQAM system when the signal is passed through a nonlinear High Power Amplifier (HPA) which is modeled as Saleh's one exhibiting amplitude and phase distortions. First, we develop an analysis showing that the FBMC-OQAM signals are more sensitive to the phase distortion than OFDM signals. Then, we address the problem of the compensation of these distortions in FBMC-OQAM systems using digital predistortion (DPD) at the transmitter. Therefore, two DPD schemes are considered in this investigation and their performance are compared in OFDM and FBMC-OQAM systems.

The nonlinearity of High Power Amplifiers (HPAs) has crucial effects on the performance of Multiple-Input Multiple-Output (MIMO). This paper analyzes a theoretical characterization of nonlinear distortion effects introduced HPA which is modeled as a nonlinear polynomial model in MIMO Output Orthogonal Frequency Division Multiplexing (OFDM) systems accompanied with Minimum Mean Square Error (MMSE) receiver. The aim of this work is to study the effect of nonlinearity on the capacity of systems and is to present an iterative estimation and compensation of the nonlinear distortion due to HPA in MIMO OFDM systems under Rayleigh fading channel. The numerical performance evaluation shows that the proposed method of compensation reduces significantly BER of MIMO OFDM systems.

In order to provide high data rate over wireless channels and improve the system capacity, Multiple-Input Multiple-Output (MIMO) wireless communication systems exploit spatial diversity by using multiple transmit and receive antennas. Moreover, to achieve high date rate and fulfill the power, MIMO systems are equipped with High Power Amplifiers (HPAs). However, HPAs cause nonlinear distortions and affect the receiver's performance. In this paper, we investigate the joint effects of HPA nonlinearity and frequency selective channel on the performance of MIMO receiver. Then, we propose two equalization schemes to compensate simultaneously nonlinear distortions and frequency selective channel effects. The first one is based on a feedforward Neural Network (NN) named (NN-MIMO-Receiver) and the second uses NN technique and LMS equalizer (LMS-NN-MIMO). The Levenberg-Marquardt algorithm (LM) is used for neural network training, which has proven [1] to exhibit a very good performance with lower computation complexity and faster convergence than other algorithms used in literature. These proposed methods are compared in term of Symbol Error Rate (SER) running under nonlinear frequency selective channel.

The combination of Multiple-Input Multiple-Output (MIMO) with Orthogonal Frequency Division Multiplexing (OFDM) is a promising technique for achieving high performance with very high data rates in 4G broadband wireless communications. Nevertheless, one major disadvantage of MIMO-OFDM systems lies in a prohibitively large Peak-to-Average Power Ratio (PAPR) of the transmitted signal on each antenna. Indeed, the performance of the receivers is very sensitive to nonlinear distortions caused by the High Power Amplifier (HPA). Furthermore, Crosstalk can take place before or after the power amplifiers designated herein as nonlinear and linear crosstalk, respectively. In this paper, we extend the efficient Neural Network Predistorter (NNPD) proposed in [1] to MIMO-OFDM systems and equally demonstrate that nonlinear crosstalk significantly affects the performance of NNPD. Along, we propose a new Crossover Neural Network Predistorter (CO-NNPD) model to compensate simultaneously for crosstalk and HPA nonlinearity in MIMO-OFDM systems. The Levenberg-Marquardt algorithm (LM) is used for neural network training, which has proven [3] to exhibit a very good performance with lower computation complexity and faster convergence than other algorithms used in literature. This paper is supported with simulation results for the Alamouti STBC MIMO OFDM system in terms of Bit Error Rate (BER) in Rayleigh fading channel.

In this paper, we consider optimizing the performance of 16-QAM orthogonal frequency division multiplexing (OFDM) signals over the non-linear, satellite transmitters. A high power amplifier produces non-linear distortions; a predistorsion technique will reduce theses distortions. The pre-distorter is based on a feed-forward neural network. In addition, the performance of the new pre-distortion scheme for OFDM systems is evaluated in terms of spectrum and total degradation BER. From simulation results, it is confirmed that the proposed pre- distorter with neural network consisting with one hidden layer and nine neurons gives a good performance improvement of quality of the transmission.